Content is one of the most essential parts of products on e-commerce websites such as eBay. It not only drives user-engagement but also traffic from various search engine websites based on the relevance. Generating the content for the products, however comes with a wide set of challenges, due to the complexity of commerce at scale, and requires new applications in text processing and information extraction to address some core issues. Some of the factors which need to be addressed are: scalability (millions of products), dynamism (products change with time), removal of item-specific or seller specific information (maintain generality), size of the content etc. Generally, curators are hired for writing the product descriptions manually, which is not cost-effective and is not scalable. In the current work, an algorithmic framework based on Natural Language Processing and Deep Learning is proposed and used to generate the content for ecommerce products. Seller descriptions for multiple items aggregated at a product level are used for content generation. Furthermore, a combination of behavioral and text signals such as search queries are also used to understand the user intent. Two different approaches are proposed in this work: Extraction (sentence retrieval) and Abstraction (sentence generation). The results of both the methods are analyzed and it is depicted that algorithmic content generation is scalable, fast and has potential to cut down the manualcuration cost dramatically.

The popularity of social media sites like Twitter and Facebook opens up interesting research opportunities for understanding the interplay of social media and e-commerce. Most research on online behavior, up until recently, has focused mostly on social media behaviors and e-commerce behaviors independently. In our study we choose a particular global e-commerce platform (eBay) and a particular global social media platform (Twitter). We quantify the characteristics of the two individual trends as well as the correlations between them. We provide evidences that about 5% of general eBay query streams show strong positive correlations with the corresponding Twitter mention streams, while the percentage jumps to around 25% for trending eBay query streams. Some categories of eBay queries, such as 'Video Games' and 'Sports', are more likely to have strong correlations. We also discover that eBay trend lags Twitter for correlated pairs and the lag differs across categories. We show evidences that celebrities' popularities on Twitter correlate well with their relevant search and sales on eBay. The correlations and lags provide predictive insights for future applications that might lead to instant merchandising opportunities for both sellers and e-commerce platforms.

In this paper, we present QSEGMENT, a real-life query segmentation system for eCommerce queries. QSEGMENT uses frequency data from the query log which we call buyers' data and also frequency data from product titles what we call sellers' data. We exploit the taxonomical structure of the marketplace to build domain specific frequency models. Using such an approach, QSEGMENT performs better than previously described baselines for query segmentation. Also, we perform a large scale evaluation by using an unsupervised IR metric which we refer to as user-intent-score. We discuss the overall architecture of QSEGMENT as well as various use cases and interesting observations around segmenting eCommerce queries.

In this paper we study diversity and its relations to search relevance in the context of an online marketplace. We conduct a large-scale log-based study using click-stream data from a leading eCommerce site. We introduce 3 main metrics -- selection (diversity), trust, and value. In our analysis we also show how these interact with relevance in different ways. We study the benefits of diversity and also show why guaranteeing diversity is important.

In today's world, brand based shopping is popular especially in product lines like clothing and shoes, appliances, and electronics. Because of the importance of brands while shopping, it has become important for online shopping portals to consider brand loyalty and brand preferences of users. In this paper, we describe a system designed for brand-based shopping and exploration. The system is built by analyzing a large query set consisting of 115M queries from eBay.com -- a vibrant marketplace with more than 95M active users. The system allows brand-pivoted exploration of inventory. It allows exploration and purchase of substitute branded goods (e.g. Sony camcorder for Canon camcorder) and complementary branded merchandise (e.g. Lego castle set for Lego train station set).

User expectation and experience for web search and eCommerce (product) search are quite different. Product descriptions are concise as compared to typical web documents. User expectation is more specific to find the right product. The difference in the publisher and searcher vocabulary (in case of product search the seller and the buyer vocabulary) combined with the fact that there are fewer products to search over than web documents result in observable numbers of searches that return no results (zero recall searches). In this paper we describe a study of zero recall searches. Our study is focused on eCommerce search and uses data from a leading eCommerce site's user click stream logs. There are 3 main contributions of our study: 1) The cause of zero recall searches; 2) A study of user's reaction and recovery from zero recall; 3) A study of differences in behavior of power users versus novice users to zero recall searches.

Query suggestion module is an integral part of every search engine. It helps search engine users narrow or broaden their searches. Published work on query suggestion methods has mainly focused on the web domain. But, the module is also popular in the domain of e-commerce for product search. In this paper, we discuss query suggestion and its methodologies in the context of e-commerce search engines. We show that dynamic inventory combined with long and sparse tail of query distribution poses unique challenges to build a query suggestion method for an e-commerce marketplace. We compare and contrast the design of a query suggestion system for web search engines and e-commerce search engines. Further, we discuss interesting measures to quantify the effectiveness of our query suggestion methodologies. We also describe the learning gained from exposing our query suggestion module to a vibrant community of millions of users.

In this paper, we describe a buzz-based recommender system based on a large source of queries in an eCommerce application. The system detects bursts in query trends. These bursts are linked to external entities like news and inventory information to find the queries currently in-demand which we refer to as buzz queries. The system follows the paradigm of limited quantity merchandising, in the sense that on a per-day basis the system shows recommendations around a single buzz query with the intent of increasing user curiosity, and improving activity and stickiness on the site. A semantic neighborhood of the chosen buzz query is selected and appropriate recommendations are made on products that relate to this neighborhood.

In this paper we describe how high quality transaction data comprising of online searching, product viewing, and product buying activity of a large online community can be used to infer semantic relationships between queries. We work with a large scale query log consisting of around 115 million queries from eBay. We discuss various techniques to infer semantic relationships among queries and show how the results from these methods can be combined to measure the strength and depict the kinds of relationships. Further, we show how this extraction of relations can be used to improve search relevance, related query recommendations, and recovery from null results in an eCommerce context.

In this paper, we present an outline of a software system for buzz-based recommendations. This system is based on a large source of queries in an eCommerce application. The buzz events are detected based on query bursts linked to external entities like news and inventory information. A semantic neighborhood of the chosen buzz query is selected and appropriate recommendations are made on products that relate to this neighborhood. The system follows the paradigm of limited quantity merchandizing, in the sense that on a per-day basis the system shows recommendations around a single buzz query with the intent of increasing user curiosity and promoting user activity and stickiness. The system demonstrates the deployment of an interesting application based on KDD principles applied to a high volume industrial context.

In large scale online systems like Search, eCommerce, or social network applications, user queries represent an important dimension of activities that can be used to study the impact on the system, and even the business. In this paper, we describe how to detect, characterize and classify bursts in user queries in a large scale eCommerce system. We build upon the approaches discussed in KDD 2002 "Bursty and Hierarchical Structure in Streams" [3] and apply them to a high volume industrial context. We describe how to identify bursts on a near real-time basis, classify them, and apply them to build interesting merchandizing applications.